Robert A. McDougal

The paper uses computer models to predict differential dendritic vulnerability to ischemia and spreading depression, finding greater calcium accumulation in basilar dendrites and higher intracellular chloride in distal apical dendrites during ischemia.

This paper evaluates using large language models to infer ion channel and receptor subtype metadata from neuroscience model code, finding LLMs outperform feature-engineered models.

This paper introduces relational homeostatic scaling, a synaptic scaling rule that enables stable rate-code transmission in neural circuits despite noise and heterogeneity.

The paper demonstrates a method to simulate neuromechanical models by integrating NEURON and MuJoCo to control musculoskeletal models with different neural control strategies.

The paper uses NLP and LLM techniques to improve the identification of computational neuroscience work and metadata from literature, enhancing ModelDB's capabilities.

This paper shows that neuronal activity can be predicted from recent synaptic events alone, reducing the need for full initial conditions or detailed differential equations.

The paper develops a multiscale computer model of spreading depolarization in brain slices, showing that SD speed depends on tissue properties and is exacerbated by hypoxia and slice thickness.

The paper modernizes the NEURON simulator to improve sustainability, portability, and performance through new tools and optimizations.

The paper develops a 3D reaction-diffusion simulation method for neurons and networks, enabling accurate modeling of dynamics in complex geometries.

This study investigates the conditions for synaptic specificity during memory maintenance by simulating protein dynamics in dendritic spines.

The paper introduces HNN, a software tool that interprets the cellular and network origin of human MEG/EEG data using a neocortical circuit model.

Open Source Brain provides a platform for visualizing, simulating, and collaboratively developing standardized neuronal and circuit models to improve model transparency and reproducibility.

NetPyNE is a tool for creating multiscale neuronal network models in NEURON using declarative specifications, enabling efficient simulation and analysis of brain circuits.